The escalating global volume of vehicles on roadways necessitates innovative solutions for efficient traffic management, heightened security, and improved access control. This paper presents the design and implementation of an Automated Car Plate Number Recognition (APNR) system developed for vehicle access control at Edo State University, Iyamho, where vehicle registration is currently performed manually at the entrance gate. The system utilises image processing, Optical Character Recognition (OCR), and database integration for real-time license plate recognition. Vehicle images captured at the entry/exit point are pre-processed using Python and OpenCV — including grayscale conversion, noise reduction, thresholding, and plate-region extraction — before recognised characters are converted to text using the Tesseract OCR engine and validated against Nigerian plate formats using regular expressions. Recognised plate numbers, together with date and time stamps, are stored in a MySQL database accessible through a PHP-based administrative dashboard. The system was evaluated through seventeen structured test cases and fifteen performance metrics covering processing speed, recognition accuracy, resource utilisation, security, and scalability. Results show a recognition accuracy of 93% on clear images, average recognition time of 1.2 seconds, and a user satisfaction rating of 4.5 out of 5.0. The system demonstrates that a lightweight, non-machine-learning APNR solution can deliver reliable, affordable vehicle identification suited to institutions with limited computing budgets.